Information processing systems increasingly utilize reconfigurable virtual resources to meet changing user needs. For example, cloud computing and storage systems implemented using virtual machines have been widely adopted. Such cloud-based systems include, for example, Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Despite the widespread availability of these and numerous other private, public and hybrid cloud offerings, there exists a significant problem in conventional practice in that there is no adequate mechanism available for efficiently migrating workloads across multiple cloud-based systems. Existing cloud-based migration approaches are commonly limited to focusing on single aspects of an application stack and are significantly time- and labor-intensive, often resulting in service interruptions. Moreover, such limitations in conventional approaches can effectively result in enterprises and users being limited and/or locked-in to a single-cloud infrastructure for increasingly sophisticated workloads. In conventional approaches, such limitations can be overcome only with tremendous and burdensome migration efforts due to cloud vendor-specific information technology (IT) system layouts and/or deployment patterns.